Plug and play self-configurable IoT gateway node for telemonitoring of ECG

Area of Science:

• Biomedical Engineering
• Internet of Things (IoT)
• Wireless Body Area Networks (WBANs)

Background:

• Efficient ECG telemonitoring is crucial in the era of IoT and hyperconnection.
• Existing systems often lack ease of use, self-configurability, and power efficiency.
• ECG signal representation parameters vary significantly, necessitating adaptive solutions.

Purpose of the Study:

• To propose a compressed sensing-based WBAN with a self-configurable gateway node (CS-SCGN).
• To achieve adaptive parameter tuning for efficient ECG signal compression and quantization.
• To investigate the use of smartphones as IoT gateway nodes for primary processing.

Main Methods:

• Utilized a deterministic binary block diagonal (DBBD) measurement matrix for compressed sensing.
• Developed a low-complexity self-configuration algorithm for adaptive parameter tuning at the gateway node.
• Implemented three distinct configuration modes to address diverse ECG remote health monitoring requirements.
• Optimized bit transmission by tuning compression ratio and quantization depth based on ECG signal dynamics.

Main Results:

• The proposed CS-SCGN system demonstrated self-configurability, enabling adaptive parameter tuning.
• Reduced communication and storage costs through optimized data transmission.
• Achieved power efficiency for sensor nodes without increasing gateway power consumption.
• Validated the system's energy efficiency and real-time feasibility using an Odroid-XU4 board, comparable to smartphone processors.

Conclusions:

• The self-configurable gateway node system offers a flexible, plug-and-play, patient-independent, and power-efficient ECG telemonitoring solution.
• The integration of smartphones as gateway nodes is feasible for primary processing in remote health monitoring.
• The adaptive tuning of compression and quantization parameters significantly enhances system performance and resource utilization.